How Process Mining Can Help Manufacturing and Assembly Lines

As industrial manufacturing companies implement a variety of business software tools, each of these systems generates a historical record of data and error reports that are proving to be a boon for manufacturing analytics professionals. Using an approach known as Process Mining, operations analysts can collect and analyze the massive amount of big data in manufacturing systems to gain insight into existing business processes, identify problems such as bottlenecks, and find ways to improve overall operational workflow.

The emergence of Process Mining as a standalone discipline is a fairly recent development in the business operations management realm. In this article, we’ll attempt to answer the frequently asked questions about what process mining is and how it fits into the world of manufacturing and assembly lines. Let’s get started!

What is Process Mining and Where Does It Fit Within the Broader Business Management Landscape?

At its most basic level, process mining is a research discipline that scoops up event data logs produced by a variety of heterogeneous enterprise systems in order to find useful information about the current state of actual business processes within the organization.

From this simple definition, it would be easy to come to the conclusion that Process Mining is the same as what’s commonly referred to as Big Data analytics. Both disciplines seek to identify cause and effect relationships that affect business performance, but there’s a major difference between the two in terms of scope.

Unlike Big Data analytics, which examines a wide variety of datasets — ranging from customer preferences to economic conditions to weather forecasts, Process Mining is by definition limited to the realm of extracting useful knowledge from event logs and other similar data sources within the organization or its supply chain.

Process Mining vs. Data Mining, image by All About Requirements

Process Mining’s ability to uncover, monitor and analyze business processes across the organization makes it a welcome new tool for professionals working in Operations Research and Business Intelligence (BI) disciplines. Insights from Process Mining analytics can be used to inform a wide variety of Business Process Improvement (BPI) tools and approaches, including:

Business Activity Monitoring (BAM)

Business Operations Management (BOM)

Complex Event Processing (BEP)

Corporate Performance Management (CPM)

Continuous Process Improvement (CPI)

Executive “Dashboards”

Kaizen / Toyota Production System (TPS)

Key Performance Indicators (KPIs)

Lean Manufacturing

Lean Six Sigma

Total Quality Management (TQM)

SCOR Business Process Modeling from APICS

Six Sigma

What are the Primary Goals of Process Mining?

Depending upon the initial state of their business process modeling efforts, businesses can expect to achieve one or more of the following Process Mining goals:

1. Discover Actual Business Processes

By looking at event logs (and taking note of process errors and exceptions), Process Mining can create useful diagrams that document the actual processes taking place within an organization, including the pathways that handle exceptions that fall out of standard processes.

2. Provide Conformance Checking Between Actual Business Processes and Assumptions Made in Process Models

Businesses that have already created business process models of their existing operations (known as the “AS-IS” condition in APICS’ SCOR terminology) can evaluate the accuracy of their models by measuring deviations from the real-world conditions as reported by Process Mining.

3. Make Enhancements and Improvements to Business Processes on an Ongoing Basis

Using Process Mining on an ongoing basis can help identify ways to improve processes across the business, such as repairing production bottlenecks or reducing errors under specific conditions. As new process improvements are introduced, Process Mining can also provide useful feedback on the efficacy of individual process changes by comparing the results to historical data records.

What’s the Relationship between Process Mining and Business Process Modeling (BPM) Frameworks and Approaches, Such as Six Sigma and SCOR?

Rather than viewing Process Mining as a threat that could displace existing process improvement frameworks, most business operations professionals welcome Process Mining as a valuable new addition to their business analysis toolkit.

For example, Process Mining’s ability to derive a current “AS-IS” process model from a wide range of event log data can help frameworks like Six Sigma and SCOR validate their assumptions about process workflow as well as identify areas that need improvement.

Can You Use Process Mining Without First Creating a Business Process Model?

The short answer is “yes,” you can implement a Process Mining system without first creating a theoretical business process model. In fact, Process Mining will build its own internal business process model as an outcome of analyzing the data logs created by enterprise software systems.

This Process Mining-generated business process model can be used as a jumping off point for further analysis by various business process improvement frameworks, such as SCOR from APICS, where it could serve as the working model for the “AS-IS” condition.

How Does Process Mining Interface with ERP, CRM, B2B and Other Business Software Tools?

Process Mining imports the historical data logs generated by these systems and uses this information as raw data for downstream analysis. In some cases, Process Mining software developers have created extensions or plug-ins to read input data directly from popular business software tools.

What Specific Benefits Does Process Mining Offer for Manufacturing Facilities and Assembly Lines?

Thanks to its ability to quickly shift through the large amount of available big data in manufacturing control systems, Process Mining can often provide specific, actionable recommendations that may elude conventional manufacturing analytics methods.

Process Mining’s manufacturing analytics approach can be used in a number of ways. For example, it can objectively compare factory output at a high level, such as between facilities in different locations or between shifts at the same facility.

It can also drill down to analyze what’s happening between any two arbitrary points in the manufacturing process to uncover a range of problems, including:

Deviations for target processes

Inventory shortages

Repeated production errors

Procurement “irregularities”

Unnecessary detours

Bottlenecks

Quality variations

What is a Petri Net Diagram and How Do You Interpret It?

As we’ve discussed, Process Mining can help create business process models from the data files produced by business software systems. It’s often a challenge to represent these process models in a comprehensive (yet understandable) way. Compounding the issue is the need to represent processes from multiple perspectives, including:

the control-flow perspective, which documents all possible activities in sequence

the organizational perspective, which identifies the people, departments or systems involved

the case perspective, which documents activities from a production element perspective; and the time perspective, which measures sequencing and frequency of operations.

Over the years, several different mathematical models (each using their own graphical notation) have been developed to represent the complexity of dynamic events that occur in the real-world. The most common representations used to diagram Process Mining control flows are:

BPMN Process. Image by Wikipedia

Business Process Model and Notation (BPMN)

EPC (Event-driven Process Chain) diagrams

Petri net (Place/transition net) diagrams

UML (Unified Modeling Language) activity diagrams

UML Activity diagram. Image by Wikipedia

For the uninitiated, the BPMN and EPC notation will seem most understandable at first glance as they bear a strong superficial resemblance to the familiar “decision tree” type of graphs. UML activity diagrams are static representations of a language model that was developed to support complex software development. Petri net diagrams, in contrast, have an appearance that at first glance may remind you of high-level electric diagrams; each of the nodes represents a different set of events (circles = conditions, bars = events, arcs = pre- or post-conditions, arrows = transitions).

Petri Net Example. Image by Wikipedia

What are some of the Challenges in Implementing Process Mining at Your Facility?

Like many information technology projects, each Process Mining implementation is different and will face its own unique set of challenges.

However, because the usefulness of Process Mining depends upon successfully interpreting event logs produced by different systems, nearly all implementations will come up against the challenge of identifying, merging, and cleaning event log data. This isn’t necessarily the fault of the systems providing the raw data, as few currently-available systems were designed with Process Mining in mind.

Consequently, implementation engineers will often need to come up with creative solutions. For example, many data logs will track the statistics for specific objects rather than the underlying processes involved. In these situations, implementation engineers will have to look for proxies that indicate process workflows in order to make the data more useful for things like manufacturing analytics.

As industrial manufacturing companies implement a variety of business software tools, each of these systems generates a historical record of data and error reports that are proving to be a boon for manufacturing analytics professionals. Using an approach known as Process Mining, operations analysts can collect and analyze the massive amount of big data in manufacturing systems to gain insight into existing business processes, identify problems such as bottlenecks, and find ways to improve overall operational workflow.

The emergence of Process Mining as a standalone discipline is a fairly recent development in the business operations management realm. In this article, we’ll attempt to answer the frequently asked questions about what process mining is and how it fits into the world of manufacturing and assembly lines. Let’s get started!

What is Process Mining and Where Does It Fit Within the Broader Business Management Landscape?

At its most basic level, process mining is a research discipline that scoops up event data logs produced by a variety of heterogeneous enterprise systems in order to find useful information about the current state of actual business processes within the organization.

From this simple definition, it would be easy to come to the conclusion that Process Mining is the same as what’s commonly referred to as Big Data analytics. Both disciplines seek to identify cause and effect relationships that affect business performance, but there’s a major difference between the two in terms of scope.

Unlike Big Data analytics, which examines a wide variety of datasets — ranging from customer preferences to economic conditions to weather forecasts, Process Mining is by definition limited to the realm of extracting useful knowledge from event logs and other similar data sources within the organization or its supply chain.

Process Mining vs. Data Mining, image by All About Requirements

Process Mining’s ability to uncover, monitor and analyze business processes across the organization makes it a welcome new tool for professionals working in Operations Research and Business Intelligence (BI) disciplines. Insights from Process Mining analytics can be used to inform a wide variety of Business Process Improvement (BPI) tools and approaches, including:

Business Activity Monitoring (BAM)

Business Operations Management (BOM)

Complex Event Processing (BEP)

Corporate Performance Management (CPM)

Continuous Process Improvement (CPI)

Executive “Dashboards”

Kaizen / Toyota Production System (TPS)

Key Performance Indicators (KPIs)

Lean Manufacturing

Lean Six Sigma

Total Quality Management (TQM)

SCOR Business Process Modeling from APICS

Six Sigma

What are the Primary Goals of Process Mining?

Depending upon the initial state of their business process modeling efforts, businesses can expect to achieve one or more of the following Process Mining goals:

1. Discover Actual Business Processes

By looking at event logs (and taking note of process errors and exceptions), Process Mining can create useful diagrams that document the actual processes taking place within an organization, including the pathways that handle exceptions that fall out of standard processes.

2. Provide Conformance Checking Between Actual Business Processes and Assumptions Made in Process Models

Businesses that have already created business process models of their existing operations (known as the “AS-IS” condition in APICS’ SCOR terminology) can evaluate the accuracy of their models by measuring deviations from the real-world conditions as reported by Process Mining.

3. Make Enhancements and Improvements to Business Processes on an Ongoing Basis

Using Process Mining on an ongoing basis can help identify ways to improve processes across the business, such as repairing production bottlenecks or reducing errors under specific conditions. As new process improvements are introduced, Process Mining can also provide useful feedback on the efficacy of individual process changes by comparing the results to historical data records.

What’s the Relationship between Process Mining and Business Process Modeling (BPM) Frameworks and Approaches, Such as Six Sigma and SCOR?

Rather than viewing Process Mining as a threat that could displace existing process improvement frameworks, most business operations professionals welcome Process Mining as a valuable new addition to their business analysis toolkit.

For example, Process Mining’s ability to derive a current “AS-IS” process model from a wide range of event log data can help frameworks like Six Sigma and SCOR validate their assumptions about process workflow as well as identify areas that need improvement.

Can You Use Process Mining Without First Creating a Business Process Model?

The short answer is “yes,” you can implement a Process Mining system without first creating a theoretical business process model. In fact, Process Mining will build its own internal business process model as an outcome of analyzing the data logs created by enterprise software systems.

This Process Mining-generated business process model can be used as a jumping off point for further analysis by various business process improvement frameworks, such as SCOR from APICS, where it could serve as the working model for the “AS-IS” condition.

How Does Process Mining Interface with ERP, CRM, B2B and Other Business Software Tools?

Process Mining imports the historical data logs generated by these systems and uses this information as raw data for downstream analysis. In some cases, Process Mining software developers have created extensions or plug-ins to read input data directly from popular business software tools.

What Specific Benefits Does Process Mining Offer for Manufacturing Facilities and Assembly Lines?

Thanks to its ability to quickly shift through the large amount of available big data in manufacturing control systems, Process Mining can often provide specific, actionable recommendations that may elude conventional manufacturing analytics methods.

Process Mining’s manufacturing analytics approach can be used in a number of ways. For example, it can objectively compare factory output at a high level, such as between facilities in different locations or between shifts at the same facility.

It can also drill down to analyze what’s happening between any two arbitrary points in the manufacturing process to uncover a range of problems, including:

Deviations for target processes

Inventory shortages

Repeated production errors

Procurement “irregularities”

Unnecessary detours

Bottlenecks

Quality variations

What is a Petri Net Diagram and How Do You Interpret It?

As we’ve discussed, Process Mining can help create business process models from the data files produced by business software systems. It’s often a challenge to represent these process models in a comprehensive (yet understandable) way. Compounding the issue is the need to represent processes from multiple perspectives, including:

the control-flow perspective, which documents all possible activities in sequence

the organizational perspective, which identifies the people, departments or systems involved

the case perspective, which documents activities from a production element perspective; and the time perspective, which measures sequencing and frequency of operations.

Over the years, several different mathematical models (each using their own graphical notation) have been developed to represent the complexity of dynamic events that occur in the real-world. The most common representations used to diagram Process Mining control flows are:

BPMN Process. Image by Wikipedia

Business Process Model and Notation (BPMN)

EPC (Event-driven Process Chain) diagrams

Petri net (Place/transition net) diagrams

UML (Unified Modeling Language) activity diagrams

UML Activity diagram. Image by Wikipedia

For the uninitiated, the BPMN and EPC notation will seem most understandable at first glance as they bear a strong superficial resemblance to the familiar “decision tree” type of graphs. UML activity diagrams are static representations of a language model that was developed to support complex software development. Petri net diagrams, in contrast, have an appearance that at first glance may remind you of high-level electric diagrams; each of the nodes represents a different set of events (circles = conditions, bars = events, arcs = pre- or post-conditions, arrows = transitions).

Petri Net Example. Image by Wikipedia

What are some of the Challenges in Implementing Process Mining at Your Facility?

Like many information technology projects, each Process Mining implementation is different and will face its own unique set of challenges.

However, because the usefulness of Process Mining depends upon successfully interpreting event logs produced by different systems, nearly all implementations will come up against the challenge of identifying, merging, and cleaning event log data. This isn’t necessarily the fault of the systems providing the raw data, as few currently-available systems were designed with Process Mining in mind.

Consequently, implementation engineers will often need to come up with creative solutions. For example, many data logs will track the statistics for specific objects rather than the underlying processes involved. In these situations, implementation engineers will have to look for proxies that indicate process workflows in order to make the data more useful for things like manufacturing analytics.